Human Breast Cancer Datasets Research Articles

Abstract IA05: Leveraging preclinical models of triple negative breast cancer for translational research

Abstract We have developed multiple novel preclinical syngeneic triple negative breast cancer(TNBC) genetically engineered mouse (GEM) models, which have been characterized genomically and with respect to their immune microenvironments. By integrating the immunological characterization of murine syngeneic mammary tumor models with analyses of human breast cancer datasets, we have demonstrated a relationship between EMT and myeloid cells, specifically tumor-associated macrophages (TAMs). We also have leveraged our syngeneic GEM models to define the response to immune checkpoint blockade therapy (ICBT) with emphasis on the myeloid cell environment. We have used our GEM models to test single agent drugs along with standard-of-care therapies for appropriate durations. Finally, we have tested these treatment regimens on established metastases with sufficient tumor burdens. Specifically, we will present results illustrating how CSF-1R targeting therapy combined with metronomic chemotherapy enhances a B and T cell response for the treatment of metastatic TNBC. In additional we well discuss how targeting the RNA helicase eIF4A that unwinds the secondary structures in the 5’UTR of mRNAs to facilitate translation initiation triggers an interferon response to suppress TNBC. Citation Format: Jeffrey M Rosen, Diego Pedroza, Na Zhao, Xiang H-F Zhang, Charles M Perou. Leveraging preclinical models of triple negative breast cancer for translational research [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr IA05.

Dissecting Spatiotemporal Structures in Spatial Transcriptomics via Diffusion-Based Adversarial Learning.

Recent advancements in spatial transcriptomics (ST) technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues. Despite these capabilities of the ST data, accurately dissecting spatiotemporal structures (e.g., spatial domains, temporal trajectories, and functional interactions) remains challenging. Here, we introduce a computational framework, PearlST (partial differential equation [PDE]-enhanced adversarial graph autoencoder of ST), for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder. PearlST employs contrastive learning to extract histological image features, integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries, and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders. Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering, trajectory inference, and pseudotime analysis. Furthermore, PearlST elucidates functional regulations of the latent features by linking intercellular ligand-receptor interactions to most contributing genes of the low-dimensional embeddings, as illustrated in a human breast cancer dataset. Overall, PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.

Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis.

Histopathology, the gold-standard technique in classifying canine mammary tumors (CMTs), is a time-consuming process, affected by high inter-observer variability. Digital (DP) and Computer-aided pathology (CAD) are emergent fields that will improve overall classification accuracy. In this study, the ability of the CAD systems to distinguish benign from malignant CMTs has been explored on a dataset-namely CMTD-of 1056 hematoxylin and eosin JPEG images from 20 benign and 24 malignant CMTs, with three different CAD systems based on the combination of a convolutional neural network (VGG16, Inception v3, EfficientNet), which acts as a feature extractor, and a classifier (support vector machines (SVM) or stochastic gradient boosting (SGB)), placed on top of the neural net. Based on a human breast cancer dataset (i.e., BreakHis) (accuracy from 0.86 to 0.91), our models were applied to the CMT dataset, showing accuracy from 0.63 to 0.85 across all architectures. The EfficientNet framework coupled with SVM resulted in the best performances with an accuracy from 0.82 to 0.85. The encouraging results obtained by the use of DP and CAD systems in CMTs provide an interesting perspective on the integration of artificial intelligence and machine learning technologies in cancer-related research.

Protein disulfide isomerase A3 activity promotes extracellular accumulation of proteins relevant to basal breast cancer outcomes in human MDA-MB-A231 breast cancer cells.

Metastasis and recurrence of breast cancer remain major causes of patient mortality, and there is an ongoing need to identify new therapeutic targets relevant to tumor invasion. Protein disulfide isomerase A3 (PDIA3) is a disulfide oxidoreductase and isomerase of the endoplasmic reticulum that has known extracellular substrates and has been correlated with aggressive breast cancers. We show that either prior PDIA3 inhibition by the disulfide isomerase inhibitor 16F16 or depletion of heparin-binding proteins strongly reduces the activity of conditioned medium (CM) of MDA-MB-231 human breast cancer cells to support promigratory cell spreading and F-actin organization by newly adherent MDA-MB-231 cells. Quantitative proteomics to investigate effects of 16F16 inhibition on heparin-binding proteins in the CM of MDA-MB-231 cells identified 80 proteins reproducibly decreased at least twofold (at q ≤ 0.05) after 16F16 treatment. By Gene Ontology analysis, many of these have roles in extracellular matrix (ECM) structure and function and cell adhesion; ribosomal proteins that also correlate with extracellular vesicles were also identified. Protein-protein interaction analysis showed that many of the extracellular proteins have known network interactions with each other. The predominant types of disulfide-bonded domains in the extracellular proteins contained β-hairpin folds, with the knottin fold the most common. From human breast cancer data sets, the extracellular proteins were found to correlate specifically with the basal subtype of breast cancer and their high expression in tumors correlated with reduced distant metastasis-free survival. These data provide new evidence that PDIA3 may be a relevant therapeutic target to alter properties of the ECM-associated microenvironment in basal breast cancer.

DNA Methyltransferase 3B-Mediated Intratumoral Heterogeneity and Therapeutic Targeting in Breast Cancer Recurrence and Metastasis.

Our findings of transcriptome changes mediated by DNMT3B provide new mechanistic insight for intratumor heterogeneity and chemoresistance, and therapeutic targeting of DNMT3B in combination with chemotherapy offer additional treatment options for metastatic disease especially for patients with TNBC.

NF-κB-dependent secretome of senescent cells can trigger neuroendocrine transdifferentiation of breast cancer cells.

Cellular senescence is characterized by a stable proliferation arrest in response to stresses and the acquisition of a senescence‐associated secretory phenotype, called SASP, composed of numerous factors including pro‐inflammatory molecules, proteases, and growth factors. The SASP affects the environment of senescent cells, especially during aging, by inducing and modulating various phenotypes such as paracrine senescence, immune cell activity, and extracellular matrix deposition and organization, which critically impact various pathophysiological situations, including fibrosis and cancer. Here, we uncover a novel paracrine effect of the SASP: the neuroendocrine transdifferentiation (NED) of some epithelial cancer cells, evidenced both in the breast and prostate. Mechanistically, this effect is mediated by NF‐κB‐dependent SASP factors, and leads to an increase in intracellular Ca2+ levels. Consistently, buffering Ca2+ by overexpressing the CALB1 buffering protein partly reverts SASP‐induced NED, suggesting that the SASP promotes NED through a SASP‐induced Ca2+ signaling. Human breast cancer dataset analyses support that NED occurs mainly in p53 WT tumors and in older patients, in line with a role of senescent cells and its secretome, as they are increasing during aging. In conclusion, our work, uncovering SASP‐induced NED in some cancer cells, paves the way for future studies aiming at better understanding the functional link between senescent cell accumulation during aging, NED and clinical patient outcome.

Abstract P1-05-08: Targeting tumor heterogeneity and breast cancer metastasis through the metastatic microenvironment mediated epigenetic reprogramming

Abstract Targeting tumor heterogeneity and breast cancer metastasis through the metastaticmicroenvironment mediated epigenetic reprogrammingLi Yang and Jae Young So, Nicolas Skrypek, Anand Merchant, George Nelson, Howard H.Yang, Maxwell P. LeeNational Cancer Institute, National Institutes of Health, Bethesda, MD 20892Current cancer treatments are largely based on the genetic characterization of primarytumors and are ineffective for metastatic disease. There is lack of mechanistic understanding ashow cancer cells are selected and evolved to establish distant metastatic colonies. In addition,tumor heterogeneity and biomarker identification remain to be some of the most difficultchallenges in cancer treatment. In the current study, we discovered an increased DNAmethyltransferase 3B (DNMT3B) in metastatic nodules and in tumor cells with epithelia tomesenchymal (EMT) phenotype. Of great interest, high levels of DNMT3B were correlated withpoor clinical outcomes in multiple human breast cancer datasets. Mechanistically, DNMT3Balters multiple pathways including STAT3, NFkappaB, PI3K/Akt, beta-catenin, Notch signalingas well as EMT, which are critical for cancer cell survival, apoptosis, proliferation, invasion, andcolonization. We further identified PGE2 and IL6 as critical inflammatory mediators inDNMT3B induction. Importantly, targeting IL6 or PGE2 production reduced DNMT3Bexpression and improved chemo treatment or PD1 immune therapy. Furthermore, perioperative(surgical removal of primary tumors) targeting DNMT3B in combination with chemotherapymarkedly suppressed tumor recurrence and metastasis in preclinical mouse models for triplenegative breast cancer. Our studies identify DNMT3B as an important mechanism fortranscription regulation in tumor heterogeneity that is critical for tumor invasion and metastasis,thus suggesting a validated target for treating metastatic disease. Citation Format: Li Yang, Jae Young So, Nicolas Skrypek, Anand Merchant, George Nelson, Howard Yang, Maxwell Lee. Targeting tumor heterogeneity and breast cancer metastasis through the metastatic microenvironment mediated epigenetic reprogramming [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-05-08.

Abstract 3111: Notch3 functions as a tumor suppressor in ER-positive breast cancer

Abstract The Notch signaling pathway has been implicated in the development as well as tumorigenesis of the mammary gland. Notch3 promotes luminal cell specification and marks a highly clonogenic luminal progenitor population. Aberrant Notch3 activation has been shown to promote the growth of basal breast cancer as well as resistance to hormonal therapy in metastatic luminal breast cancer. Interestingly, recent studies also suggest a tumor-suppressive function of Notch3 in mammary epithelium through regulation of estrogen receptor α (ER). Here we report the expression and functional analysis of Notch3 in the mouse mammary gland. X-gal staining of the Notch3β-Geo/+ mammary gland showed that Notch3 was highly expressed in luminal cells throughout mammary development with exception at the initiation of post-lactational involution, when Notch3 expression was restricted to the basal cells. Deletion of Notch3 caused decreased Notch activation in the CD24HiCD49fLo subpopulation of the pubescent mammary gland, accompanied by a significant decrease in the number of CD24HiCD49fLoCD61+ luminal progenitor cells. Whole-mount preparation of the Notch3 knockout mammary glands showed normal development during puberty, pregnancy, lactation and involution. Surprisingly, parous Notch3 knockout mice developed mammary ductal hyperplasia by 10 months of age, some of which progressed to ductal carcinoma in situ, and ultimately to invasive and metastatic cancer. Parous Notch3 knockout mice exhibited an expansion of the CD24HiCD49fLo subpopulation, and mammary tumors from these mice were composed predominantly of CD24HiCD49fLo cells. The vast majority of these tumors were ER-positive and relatively well differentiated, with a papillary and/or glandular pattern. They expressed the luminal marker cytokeratin 8, and some of them co-expressed cytokeratin 14, a basal marker. All Notch3 knockout mammary tumors showed high level expression of Cyclin D1. These results suggest that Notch3 may prevent ER-positive breast cancer through suppression of CD24HiCD49fLo progenitor cell self-renewal in the postpartum mammary gland. In addition to its impact on mammary epithelium, deletion of Notch3 altered the mammary microenvironment, in particular, brown adipocyte differentiation in the mammary fat pad. Further experiments will be performed to define a potential role for Notch3-regulated brown adipose tissue in breast cancer initiation and progression. Finally, analysis of TCGA and METABRIC human breast cancer data sets revealed a negative correlation between expressions of NOTCH3 and ESR1 (encoding estrogen receptor α), and association of high NOTCH3 expression with enhanced survival of patients with luminal A and luminal B subtype breast tumors. Taken together, Notch3 can act as a tumor suppressor in the mammary epithelium, and possibly in the mammary microenvironment as well, to prevent development and metastasis of ER-positive breast cancer. Citation Format: Wen-Cheng Chung, Sean E. Egan, Keli Xu. Notch3 functions as a tumor suppressor in ER-positive breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3111.

CBFB cooperates with p53 to maintain TAp73 expression and suppress breast cancer.

The CBFB gene is frequently mutated in several types of solid tumors. Emerging evidence suggests that CBFB is a tumor suppressor in breast cancer. However, our understanding of the tumor suppressive function of CBFB remains incomplete. Here, we analyze genetic interactions between mutations of CBFB and other highly mutated genes in human breast cancer datasets and find that CBFB and TP53 mutations are mutually exclusive, suggesting a functional association between CBFB and p53. Integrated genomic studies reveal that TAp73 is a common transcriptional target of CBFB and p53. CBFB cooperates with p53 to maintain TAp73 expression, as either CBFB or p53 loss leads to TAp73 depletion. TAp73 re-expression abrogates the tumorigenic effect of CBFB deletion. Although TAp73 loss alone is insufficient for tumorigenesis, it enhances the tumorigenic effect of NOTCH3 overexpression, a downstream event of CBFB loss. Immunohistochemistry shows that p73 loss is coupled with higher proliferation in xenografts. Moreover, TAp73 loss-of-expression is a frequent event in human breast cancer tumors and cell lines. Together, our results significantly advance our understanding of the tumor suppressive functions of CBFB and reveal a mechanism underlying the communication between the two tumor suppressors CBFB and p53.

BCL-XL blockage in TNBC models confers vulnerability to inhibition of specific cell cycle regulators.

Cell cycle regulators are frequently altered in Triple-Negative Breast Cancer (TNBC). Emerging agents targeting these signals offer the possibility to design new combinatorial therapies. However, preclinical models that recapitulate TNBC primary resistance and heterogeneity are essential to evaluate the potency of these combined treatments.Methods: Bioinformatic processing of human breast cancer datasets was used to analyse correlations between expression levels of cell cycle regulators and patient survival outcome. The MMTV-R26Met mouse model of TNBC resistance and heterogeneity was employed to analyse expression and targeting vulnerability of cell cycle regulators in the presence of BCL-XL blockage. Robustness of outcomes and selectivity was further explored using a panel of human breast cancer cells. Orthotopic studies in nude mice were applied for preclinical evaluation of efficacy and toxicity. Alterations of protein expression, phosphorylation, and/or cellular localisation were analysed by western blots, reverse phase protein array, and immunocytochemistry. Bioinformatics was performed to highlight drug's mechanisms of action.Results: We report that high expression levels of the BCL2L1 gene encoding BCL-XL and of specific cell cycle regulators correlate with poor survival outcomes of TNBC patients. Blockage of BCL-XL confers vulnerability to drugs targeting CDK1/2/4, but not FOXM1, CDK4/6, Aurora A and Aurora B, to all MMTV-R26Met and human TNBC cell lines tested. Combined blockage of BCL-XL and CDK1/2/4 interfered with tumour growth in vivo. Mechanistically, we show that, co-targeting of BCL-XL and CDK1/2/4 synergistically inhibited cell viability by combinatorial depletion of survival and RTK/AKT signals, and concomitantly restoring FOXO3a tumour suppression actions. This was accompanied by an accumulation of DNA damage and consequently apoptosis.Conclusions: Our studies illustrate the possibility to exploit the vulnerability of TNBC cells to CDK1/2/4 inhibition by targeting BCL-XL. Moreover, they underline that specificity matters in targeting cell cycle regulators for combinatorial anticancer therapies.

The ZNF750\u2013RAC1 axis as potential prognostic factor for breast cancer

The human zinc finger (C2H2-type) protein ZNF750 is a transcription factor regulated by p63 that plays a critical role in epithelial tissues homoeostasis, as well as being involved in the pathogenesis of cancer. Indeed, missense mutations, truncation and genomic deletion have been found in oesophageal squamous cell carcinoma. In keeping, we showed that ZNF750 negatively regulates cell migration and invasion in breast cancer cells; in particular, ZNF750 binds and recruits KDM1A and HDAC1 on the LAMB3 and CTNNAL1 promoters. This interaction, in turn, represses the transcription of LAMB3 and CTNNAL1 genes, which are involved in cell migration and invasion. Given that ZNF750 is emerging as a crucial transcription factor that acts as tumour suppressor gene, here, we show that ZNF750 represses the expression of the small GTPase, Ras-related C3 botulinum toxin substrate 1 (RAC1) in breast cancer cell lines, by directly binding its promoter region. In keeping with ZNF750 controlling RAC1 expression, we found an inverse correlation between ZNF750 and RAC1 in human breast cancer datasets. More importantly, we found a significant upregulation of RAC1 in human breast cancer datasets and we identified a direct correlation between RAC1 expression and the survival rate of breast cancer patient. Overall, our findings provide a novel molecular mechanism by which ZNF750 acts as tumour suppressor gene. Hence, we report a potential clinical relevance of ZNF750/RAC1 axis in breast cancer.

A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research

Canine mammary carcinoma (CMC) has been used as a model to investigate the pathogenesis of human breast cancer and the same grading scheme is commonly used to assess tumor malignancy in both. One key component of this grading scheme is the density of mitotic figures (MF). Current publicly available datasets on human breast cancer only provide annotations for small subsets of whole slide images (WSIs). We present a novel dataset of 21 WSIs of CMC completely annotated for MF. For this, a pathologist screened all WSIs for potential MF and structures with a similar appearance. A second expert blindly assigned labels, and for non-matching labels, a third expert assigned the final labels. Additionally, we used machine learning to identify previously undetected MF. Finally, we performed representation learning and two-dimensional projection to further increase the consistency of the annotations. Our dataset consists of 13,907 MF and 36,379 hard negatives. We achieved a mean F1-score of 0.791 on the test set and of up to 0.696 on a human breast cancer dataset.

Reduced expression of ppGalNAc-T4 promotes proliferation of human breast cancer cells.

Breast cancer, one of the most frequently diagnosed and aggressive malignancies, is the major cause of cancer-related death greatly threatening women health. Polypeptide N-acetylgalactosaminyltransferase 4 (ppGalNAc-T4), responsible for the initial step of mucin-type O-glycosylation, has been reported to be implicated in diverse types of human tumors. However, the biological role of ppGalNAc-T4 in breast cancer is still undetermined. In this study, we investigate the effects and mechanism of ppGalNAc-T4 to breast cancer cell proliferation. From analysis of high throughput RNA sequencing datasets of Gene Expression Omnibus and ArrayExpress, a positive correlation between ppGalNAc-T4 and the recurrence-free survival was observed in multigroup of human breast cancer datasets. Moreover, transcriptomes analysis using RNA-sequencing in MCF7 cells revealed that cell cycle-related genes induced the effects of ppGalNAc-T4 on breast cancer cell proliferation. Additionally, investigations showed that ppGalNAc-T4 impaired cell proliferation in MCF-7 and MDA-MB-231 breast cells. Furthermore, our results suggested that the ppGalNAc-T4 knockout activated Notch signaling pathway and enhanced cell proliferation. Collectively, our data indicated that ppGalNAc-T4 affected the proliferation of human breast cancer cells, which appears to be a novel target for understanding the underlying molecular mechanism of breast cancer.

Breast Cancer Cell-Neutrophil Interactions Enhance Neutrophil Survival and Pro-Tumorigenic Activities.

Simple SummaryTumor cell–neutrophil interactions play an important role in tumor progression, metastasis, and overall survival. The purpose of this study was to examine the tumor cell–neutrophil survival and function. We observed that high neutrophil infiltration was associated with disease aggressiveness and therapy resistance, and breast cancer-derived factors significantly enhanced neutrophil survival, polarization, and expression of pro-inflammatory cytokines. The present study demonstrates the significance of tumor cell–neutrophil interaction in breast therapy resistance and neutrophils as a potential therapeutic target. Breast cancer remains the most prevalent cancer in women with limited treatment options for patients suffering from therapy-resistance and metastatic disease. Neutrophils play an important role in breast cancer progression and metastasis. We examined the pro-tumorigenic nature of the breast cancer cell–neutrophil interactions and delineated the differences in neutrophil properties between the chemotherapy-resistant and the parent tumor microenvironment. Our data demonstrated that high neutrophil infiltration is associated with disease aggressiveness and therapy resistance. In the human breast cancer dataset, expression of neutrophil-related signature gene expression was higher in tumors from therapy-resistant patients than therapy-sensitive patients. We observed that breast cancer-derived factors significantly enhanced neutrophil survival, polarization, and pro-inflammatory cytokine expression. Breast cancer cell-derived supernatant treated neutrophils significantly expressed high levels of interleukin-1β (IL-1β), CC-chemokine ligand-2-4 (CCL2, CCL3, CCL4), inducible nitric oxide synthase (iNOS), and matrix metallopeptidase-9 (MMP9), and formed extracellular traps (NETs). Moreover, neutrophils showed increased secretion of MMP9 when cultured with the supernatant of chemotherapy-resistant Cl66-Doxorubicin (Cl66-Dox) and Cl66-Paclitaxel (Cl66-Pac) cells in comparison with the supernatant of Cl66-parent cells. Together, these data suggest an important role of breast cancer cell–neutrophil interactions in regulating pro-tumor characteristics in neutrophils and its modulation by therapy resistance.

An embedded gene selection method using knockoffs optimizing neural network

BackgroundGene selection refers to find a small subset of discriminant genes from the gene expression profiles. How to select genes that affect specific phenotypic traits effectively is an important research work in the field of biology. The neural network has better fitting ability when dealing with nonlinear data, and it can capture features automatically and flexibly. In this work, we propose an embedded gene selection method using neural network. The important genes can be obtained by calculating the weight coefficient after the training is completed. In order to solve the problem of black box of neural network and further make the training results interpretable in neural network, we use the idea of knockoffs to construct the knockoff feature genes of the original feature genes. This method not only make each feature gene to compete with each other, but also make each feature gene compete with its knockoff feature gene. This approach can help to select the key genes that affect the decision-making of neural networks.ResultsWe use maize carotenoids, tocopherol methyltransferase, raffinose family oligosaccharides and human breast cancer dataset to do verification and analysis.ConclusionsThe experiment results demonstrate that the knockoffs optimizing neural network method has better detection effect than the other existing algorithms, and specially for processing the nonlinear gene expression and phenotype data.

Classification of gene expression patterns using a novel type-2 fuzzy multigranulation-based SVM model for the recognition of cancer mediating biomarkers

In this article, we propose a novel type-2 fuzzy multigranulation-based SVM model for gene expression pattern classification on human breast cancer dataset. Firstly, a type-2 fuzzy multigranulation system has been designed for the classification task dealing with noisy and nonlinear microarray gene expression patterns. Thereafter, the fuzzy if–then rules have been devised on the feature vectors of microarray to enable accurate bilinear classification process. The fuzzy if–then rules in the domain of type-2 fuzzy multigranulation system are able to identify efficient expression patterns that have been deferentially expressed from normal state to carcinogenic state. The proposed method reduces the structural complexity of the fuzzy if–then rules (type 1) since it works on upper and lower membership functions instead of a single membership function. In addition, a fuzzy rough approximation has been utilized in the model to reduce the computational cost. Lastly, the association among genes consisting of significantly different expression patterns from normal state to malignant state has been recognized with respect to their nature. The effectiveness of the proposed method has been implemented on eight microarray gene expression datasets for human breast cancer patients. Moreover, we have validated the results by F-score and NCBI database which signify that the proposed model performs better in comparison with the existing methods.

Randomized nonlinear one-class support vector machines with bounded loss function to detect of outliers for large scale IoT data

Exponential growth of large scale data industrial internet of things is evident due to the enormous deployment of IoT data acquisition devices. Detection of unusual patterns from large scale IoT data is important though challenging task. Recently, one-class support vector machines is extensively being used for anomaly detection. It tries to find an optimal hyperplane in high dimensional data that best separates the data from anomalies with maximum margin. However, the hinge loss of traditional one-class support vector machines is unbounded, which results in larger loss caused by outliers affecting its performance for anomaly detection. Furthermore, existing methods are computationally complex for larger data. In this paper, we present novel anomaly detection for large scale data by using randomized nonlinear features in support vector machines with bounded loss function rather than finding optimized support vectors with unbounded loss function. Extensive experimental evaluation on ten benchmark datasets shows the robustness of the proposed approach against outliers such as 0.8239, 0.7921 , 0.7501, 0.6711 , 0.6692, 0.4789 , 0.6462 , 0.6812 , 0.7271 and 0.7873 accuracy for Gas Sensor Array, Human Activity Recognition, Parkinson’s, Hepatitis, Breast Cancer, Blood Transfusion , Heart, ILPD and Wholesale Customers datasets respectively. In addition to this, introduction of randomized nonlinear feature helps to considerably decrease the computational complexity and space complexity from O(N3) to O(Bkn) and O(N2) to O(Bkn). Thus, very attractive for larger datasets.

Peptidylarginine Deiminase IV Regulates Breast Cancer Stem Cells via a Novel Tumor Cell–Autonomous Suppressor Role

Peptidylarginine deiminases (PADI) catalyze posttranslational modification of many target proteins and have been suggested to play a role in carcinogenesis. Citrullination of histones by PADI4 was recently implicated in regulating embryonic stem and hematopoietic progenitor cells. Here, we investigated a possible role for PADI4 in regulating breast cancer stem cells. PADI4 activity limited the number of cancer stem cells (CSC) in multiple breast cancer models in vitro and in vivo. Mechanistically, PADI4 inhibition resulted in a widespread redistribution of histone H3, with increased accumulation around transcriptional start sites. Interestingly, epigenetic effects of PADI4 on the bulk tumor cell population did not explain the CSC phenotype. However, in sorted tumor cell populations, PADI4 downregulated expression of master transcription factors of stemness, NANOG and OCT4, specifically in the cancer stem cell compartment, by reducing the transcriptionally activating H3R17me2a histone mark at those loci; this effect was not seen in the non-stem cells. A gene signature reflecting tumor cell-autonomous PADI4 inhibition was associated with poor outcome in human breast cancer datasets, consistent with a tumor-suppressive role for PADI4 in estrogen receptor-positive tumors. These results contrast with known tumor-promoting effects of PADI4 on the tumor stroma and suggest that the balance between opposing tumor cell-autonomous and stromal effects may determine net outcome. Our findings reveal a novel role for PADI4 as a tumor suppressor in regulating breast cancer stem cells and provide insight into context-specific effects of PADI4 in epigenetic modulation. SIGNIFICANCE: These findings demonstrate a novel activity of the citrullinating enzyme PADI4 in suppressing breast cancer stem cells through epigenetic repression of stemness master transcription factors NANOG and OCT4.

LMSM: Amodular approach for identifying lncRNA related miRNA sponge modules in breast cancer.

Until now, existing methods for identifying lncRNA related miRNA sponge modules mainly rely on lncRNA related miRNA sponge interaction networks, which may not provide a full picture of miRNA sponging activities in biological conditions. Hence there is a strong need of new computational methods to identify lncRNA related miRNA sponge modules. In this work, we propose a framework, LMSM, to identify LncRNA related MiRNA Sponge Modules from heterogeneous data. To understand the miRNA sponging activities in biological conditions, LMSM uses gene expression data to evaluate the influence of the shared miRNAs on the clustered sponge lncRNAs and mRNAs. We have applied LMSM to the human breast cancer (BRCA) dataset from The Cancer Genome Atlas (TCGA). As a result, we have found that the majority of LMSM modules are significantly implicated in BRCA and most of them are BRCA subtype-specific. Most of the mediating miRNAs act as crosslinks across different LMSM modules, and all of LMSM modules are statistically significant. Multi-label classification analysis shows that the performance of LMSM modules is significantly higher than baseline's performance, indicating the biological meanings of LMSM modules in classifying BRCA subtypes. The consistent results suggest that LMSM is robust in identifying lncRNA related miRNA sponge modules. Moreover, LMSM can be used to predict miRNA targets. Finally, LMSM outperforms a graph clustering-based strategy in identifying BRCA-related modules. Altogether, our study shows that LMSM is a promising method to investigate modular regulatory mechanism of sponge lncRNAs from heterogeneous data.

The Outcome of TGFβ Antagonism in Metastatic Breast Cancer Models In Vivo Reflects a Complex Balance between Tumor-Suppressive and Proprogression Activities of TGFβ.

TGFβs are overexpressed in many advanced cancers and promote cancer progression through mechanisms that include suppression of immunosurveillance. Multiple strategies to antagonize the TGFβ pathway are in early-phase oncology trials. However, TGFβs also have tumor-suppressive activities early in tumorigenesis, and the extent to which these might be retained in advanced disease has not been fully explored. A panel of 12 immunocompetent mouse allograft models of metastatic breast cancer was tested for the effect of neutralizing anti-TGFβ antibodies on lung metastatic burden. Extensive correlative biology analyses were performed to assess potential predictive biomarkers and probe underlying mechanisms. Heterogeneous responses to anti-TGFβ treatment were observed, with 5 of 12 models (42%) showing suppression of metastasis, 4 of 12 (33%) showing no response, and 3 of 12 (25%) showing an undesirable stimulation (up to 9-fold) of metastasis. Inhibition of metastasis was immune-dependent, whereas stimulation of metastasis was immune-independent and targeted the tumor cell compartment, potentially affecting the cancer stem cell. Thus, the integrated outcome of TGFβ antagonism depends on a complex balance between enhancing effective antitumor immunity and disrupting persistent tumor-suppressive effects of TGFβ on the tumor cell. Applying transcriptomic signatures derived from treatment-naïve mouse primary tumors to human breast cancer datasets suggested that patients with breast cancer with high-grade, estrogen receptor-negative disease are most likely to benefit from anti-TGFβ therapy. Contrary to dogma, tumor-suppressive responses to TGFβ are retained in some advanced metastatic tumors. Safe deployment of TGFβ antagonists in the clinic will require good predictive biomarkers.